pages/about.py

import streamlit as st
from menu import menu_with_redirect

# Path manipulation
from pathlib import Path

# Custom and other imports
import project_config

# Redirect to app.py if not logged in, otherwise show the navigation menu
menu_with_redirect()

# Header
st.image(str(project_config.MEDIA_DIR / 'about_header.svg'), use_column_width=True)

# Main content
st.markdown(f"Hello, {st.session_state.name}! Welcome to GRAVITY, a **GR**aph **A**I **VI**sualization **T**ool to query and visualize knowledge graph-grounded biomedical AI models.")

# Subheader
st.subheader("About GRAVITY", divider = "grey")

st.markdown("""
Knowledge graphs (KGs) are data structures that use network topology to represent relational information, including and especially in biology and medicine. Graph artificial intelligence (AI) models trained on these biomedical KGs can enable many important link prediction tasks, such as predicting disease progression, diagnosing genetic disorders, identifying therapeutic targets, and discovering new drugs. However, especially in biomedical settings, it is important for clinicians and scientists to evaluate whether KG-grounded AI models are safe and trustworthy, and whether the predictions of these models are biologically explainable. To address this challenge, we developed GRAVITY, an interactive user interface for graph-based explainable AI. GRAVITY enables human users to query and interpret KG-grounded AI models for biomedical link prediction tasks.
""")